eddie for investment opportunities forecasting

EDDIE for Investment

Opportunities Forecasting

Michael Kampouridishttp://kampouridis.net/

Email: mkampo [at] essex [dot] ac [dot] uk

OutlinePresentation of EDDIE 8

EDDIE 8-TEACH demonstration

Comprehensive exercises

EDDIE’s goalEDDIE is a GP tool that attempts to answer the following question:“Will the price of the X stock go up by r%

within the next n days”?Users specify X, r, and n

How EDDIE works

Financial ExpertFinancial Expert

Genetic Decision Tree(GDT)

Genetic Decision Tree(GDT)

EDDIEEDDIE5. Approval / rejection

1. Suggestion of indicators

3. Evaluate

Training DataTraining Data

2. Output

Training DataTraining Data

Testing DataTesting Data

4. Apply

How the training data is created

GivenGiven

Daily Daily closingclosing

9090999987878282

……....

Expert Expert adds:adds:

50 50 days days M.A.M.A.

8080

8282

8383

8282

…….. ..

More More input:input:

12 12 days days VolVol

5050

5252

5353

5151

…….. ..

Define Define targettarget

::

4% in 4% in 20 20

days?days?

11

00

11

11

…….. ..

…….. ..

A typical GDT: EDDIE 8Fu

nctio

ns

VarConstructor >

If-then-else

Buy (1)Not Buy (0)

If-then-else

Buy (1)

6.4

<

Term

inal

s

VarConstructor

5.57MA 12

Momentum 50

EDDIE 8: Technical Indicators

Technical Indicator (Abbreviation)

Moving Average (MA)

Trade Break Out (TBR)

Filter (FLR)

Volatility (Vol)

Momentum (Mom)

Momentum Moving Average (MomMA)

GP ProcessInitialise population

Calculate fitness of each tree in the population

Selection of individuals for producing new offspring by the means of different genetic operators (e.g. crossover, mutation). These offspring form the new population

Repeat the previous two steps for a number of generations N

Performance Measures

Rate of Correctness (RC) = (TN + TP) Rate of Correctness (RC) = (TN + TP) TotalTotal

Rate of Failure (RF) = FP Rate of Failure (RF) = FP (FP + TP) (FP + TP) Rate of Missing Chances (RMC) = FN Rate of Missing Chances (RMC) = FN (FN+TP)(FN+TP)

Fitness Function (ff) = = w1*RC-w2*RMC-*RC-w2*RMC-w3*RFw3*RF

Negative

True Negative

False Negative

Predictions

Positive

False Positive

True Positive

Reality

Negative

Positive

Thanks • You can find these slides on my website, under the teaching tab:– http://kampouridis.net/teaching/cf963

• Any other material that we use today (EDDIE 8-Teaching, Lab sheet) can also be found there

• If you have any questions, feel free to email me. I’m happy to arrange a meeting

• EDDIE 8-Teaching Demo + Comprehensive exercises

MSc dissertation topic• There are a couple of extensions to EDDIE 8, which would

fit very well as an MSc dissertation topic

• You would be given the source code of EDDIE and be asked to add some new java code, which would be related to heuristic search methods– Java knowledge is required– No need to have implemented heuristics algorithms before.

• You would then apply EDDIE 8 to a different stocks and investigate on the advantages of the introduction of heuristics to the search process of EDDIE 8

• Opportunity for those who are interested in a project that has real-life/industry application– Attract industry’s interest– Do actual research– Possibility of publishing the results in a paper

Supplementary Material

Constraints in the Fitness Function

• ff = w1’*RC-w2*RMC-w3*RF

• Constraint R = [Cmin, Cmax]where Cmin = (Pmin/Ntr) x 100%,Cmax = (Pmax/Ntr) x 100%,

0<= Cmin <= Cmax <= 100% Ntr is the total number of training data casesPmin is the minimum number of positive predictions requiredPmax is the maximum number of positive predictions required

If the percentage of positive signals predicted falls in the range of constraint R, then w1’ = w1. If not, then w1’ = 0.

In the latter case, the GDT is heavily penalized and ends up with a negative fitness function